Defend against frontier cyber models: Cloudflare's architecture as customer zero

Defend against frontier cyber models: Cloudflare's architecture as customer zero Defend against frontier cyber models: Cloudflare's architecture as customer zero 2026-06-09 Rohit Chenna Reddy

Chase Catelli

Dan Jones

8 min read

A few weeks ago, we wrote about Project Glasswing and what we observed when we pointed cyber frontier models at our own code. Since then, we’ve seen that the part of the post that has resonated most deeply is the argument that the architecture around the vulnerability matters more than the speed of the patch. In the conversations we've had with CISOs and security teams since, the questions have been consistent: what does our architecture actually look like, what should we monitor for, where do we start, and how can Cloudflare help? Before getting into the details: the architecture below is built almost entirely from Cloudflare's own products, because Cloudflare security is customer zero for the security products we build. The Cloudflare stack already exists in front of our code, employees, and customer-facing applications. If you're a Cloudflare customer, every layer below is available to you today. If you're not, the principles still apply to whatever stack you've built.

What a cyber frontier model actually changes

In the previous post , we showed how a cyber frontier model like Mythos changes the attacker’s timeline. It can find vulnerabilities, reason through exploit chains, and generate working proofs faster than earlier models. While models like Mythos do not change the shape of an intrusion — reconnaissance, initial access, lateral movement, persistence, and exfiltration still have to happen — the difference is in the speed and scale. When pointed at the open web, a model can find and hit low-hanging fruit quickly. Against a hardened target, it still has to probe, and adapt, and it often produces more noise than a careful human operator would. Discovery, exploit chain construction, and proof-of-concept generation used to be the gating constraints on producing a working attack. A frontier model handles all three in a fraction of the time. Work that used to be slow and methodical is now fast and indiscriminate. While AI is accelerating how fast developer teams at Cloudflare and many other companies can ship code, the security team’s work has not compressed the same way. An attacker only needs one opening to get in, while security teams need to find and close them all. Writing a fix, regressing it, and shipping it without breaking the code around it has constraints that AI doesn't remove. We learned this the hard way when we let an AI coding assistant write its own patches against our own bugs, as we described at the end of the previous post . Some of those patches fixed the original bug while quietly breaking something else the code depended on. As these models become more competent and capable, our main focus from a threat standpoint comes down to three things. Each one shapes the architecture we walk through in the rest of this post. The first is the speed of discovery.  Frontier models make it easier to search large bodies of public code, including the open-source libraries that many companies depend on. That does not mean every bug in a library is exploitable, or that library bugs are where most vulnerabilities live. Exploitability still depends on how the code is used, whether attacker-controlled input can reach the vulnerable path, and the protections that sit around it. But widely used open-source libraries and frameworks give attackers a shared surface to study at scale. When a real, reachable vulnerability exists there, a model can help find it, reason about possible exploit paths, and generate proof-of-concept variants faster than maintainers and defenders can review every downstream use. The gap between when an attacker discovers a vulnerability and when defenders learn it exists is what worries us most. If you are not running these models against your own code, it is safe to assume someone else is.

The second is exploit volume and adaptation. A model can produce thousands of variations of a single exploit and run reconnaissance at the same scale. All that volume gives an attacker an advantage, but it won’t necessarily get them past signature-based detections. Many of those iterations will have the same underlying signature, so a rule that catches the first one will catch the rest. Adaptation is how they will get past signature-based detections. Ask a model to show you a SQL injection, and it will return a textbook example. Tell it there is a WAF in the way, and it will start probing, learning what gets blocked, and rewriting the payload until it can slip past the rule blocking it.

The third is the impact when a vulnerability is inevitably exploited. No architecture catches everything. After the vulnerability is exploited, the question we ask ourselves is: where can the attacker get to with one identity, one path, or one credential, before something else stops them? If the answer is "anywhere they want," the vulnerability was never the problem. The architecture around the vulnerability was.

Cloudflare’s superpower: visibility

We see roughly a fifth of the web and that tells us, in real time, which payloads are mutating, which patterns are picking up, and where attacker tooling is moving next. Two teams turn that visibility into defense. First is Cloudforce One , our threat intelligence, research, and operations team, which sits within the Cloudflare security organization. They turn what we see across the network into insights the rest of the stack can act on: tracked adversaries, emerging campaigns, and indicators of compromise (IOCs). The hard part of this work was never knowing what is malicious — it was the delay in mitigation. Knowledge of a new threat normally has to travel from a threat report, into a feed, and then into a company’s defense before it can be used to block anything. Attackers have learned to move faster than that. Our network closes that gap: Cloudflare customers can now use Cloudforce One threat intelligence directly within the WAF to block high-risk traffic. Second is the team that owns the WAF engine that does the actual detecting: the managed rulesets that run in front of our own properties and are available to every Cloudflare customer, the machine learning behind WAF Attack Score , and the relationships that sometimes let us ship a rule before a CVE is publicly disclosed. The team is globally…